{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Selecting useful features\n",
    "\n",
    "When looking at the second model's feature importances, we saw that the TF-IDF vectorization features were suspiciously absent from the most important features we displayed. To verify that a model can perform well without these features, let's omit them and train a third model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package vader_lexicon to\n",
      "[nltk_data]     /Users/emmanuel.ameisen/nltk_data...\n",
      "[nltk_data]   Package vader_lexicon is already up-to-date!\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from pathlib import Path\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import accuracy_score, f1_score, precision_score, recall_score, classification_report\n",
    "from sklearn.externals import joblib\n",
    "import sys\n",
    "sys.path.append(\"..\")\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "from ml_editor.data_processing import (\n",
    "    format_raw_df,\n",
    "    get_split_by_author\n",
    ")\n",
    "from ml_editor.model_v2 import (\n",
    "    add_char_count_features,\n",
    "    get_word_stats,\n",
    "    get_sentiment_score,\n",
    "    POS_NAMES,\n",
    "    get_question_score_from_input\n",
    ")\n",
    "from ml_editor.model_evaluation import (\n",
    "    get_feature_importance,\n",
    "    get_roc_plot,\n",
    "    get_confusion_matrix_plot,\n",
    "    get_calibration_plot\n",
    ")\n",
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "np.random.seed(35)\n",
    "\n",
    "data_path = Path('../data/writers.csv')\n",
    "df = pd.read_csv(data_path)\n",
    "df = format_raw_df(df.copy())\n",
    "\n",
    "df = df.loc[df[\"is_question\"]].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"full_text\"] = df[\"Title\"].str.cat(df[\"body_text\"], sep=\" \", na_rep=\"\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The function below add features. Feel free to check out the ml_editor source code to see more about what these functions are doing!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 7971/7971 [04:29<00:00, 29.52it/s]\n",
      "100%|██████████| 7971/7971 [00:16<00:00, 474.30it/s]\n"
     ]
    }
   ],
   "source": [
    "df = add_char_count_features(df.copy())\n",
    "df = get_word_stats(df.copy())\n",
    "df = get_sentiment_score(df.copy())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "feature_arr = [\"num_questions\", \n",
    "               \"num_periods\",\n",
    "               \"num_commas\",\n",
    "               \"num_exclam\",\n",
    "               \"num_quotes\",\n",
    "               \"num_colon\",\n",
    "               \"num_stops\",\n",
    "               \"num_semicolon\",\n",
    "               \"num_words\",\n",
    "               \"num_chars\",\n",
    "               \"num_diff_words\",\n",
    "               \"avg_word_len\",\n",
    "               \"polarity\"\n",
    "              ]\n",
    "feature_arr.extend(POS_NAMES.keys())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model\n",
    "\n",
    "Now that we've added our new features, let's train a new model. We'll use the same architecture as before, but with new features. You can visualize the new features below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# We split again since we have now added all features. \n",
    "train_df, test_df = get_split_by_author(df, test_size=0.2, random_state=40)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>num_questions</th>\n",
       "      <th>num_periods</th>\n",
       "      <th>num_commas</th>\n",
       "      <th>num_exclam</th>\n",
       "      <th>num_quotes</th>\n",
       "      <th>num_colon</th>\n",
       "      <th>num_stops</th>\n",
       "      <th>num_semicolon</th>\n",
       "      <th>num_words</th>\n",
       "      <th>num_chars</th>\n",
       "      <th>...</th>\n",
       "      <th>NOUN</th>\n",
       "      <th>NUM</th>\n",
       "      <th>PART</th>\n",
       "      <th>PRON</th>\n",
       "      <th>PROPN</th>\n",
       "      <th>PUNCT</th>\n",
       "      <th>SCONJ</th>\n",
       "      <th>SYM</th>\n",
       "      <th>VERB</th>\n",
       "      <th>X</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>56.818182</td>\n",
       "      <td>56.818182</td>\n",
       "      <td>28.409091</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1022.727273</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2073.863636</td>\n",
       "      <td>35200</td>\n",
       "      <td>...</td>\n",
       "      <td>3.693182</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.136364</td>\n",
       "      <td>1.420455</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.988636</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.113636</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>90.634441</td>\n",
       "      <td>30.211480</td>\n",
       "      <td>60.422961</td>\n",
       "      <td>0.0</td>\n",
       "      <td>60.422961</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1087.613293</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2024.169184</td>\n",
       "      <td>33100</td>\n",
       "      <td>...</td>\n",
       "      <td>4.229607</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.510574</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.416918</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.719033</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>175.438596</td>\n",
       "      <td>87.719298</td>\n",
       "      <td>87.719298</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1491.228070</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2719.298246</td>\n",
       "      <td>11400</td>\n",
       "      <td>...</td>\n",
       "      <td>4.385965</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.385965</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.508772</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.894737</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>70.257611</td>\n",
       "      <td>46.838407</td>\n",
       "      <td>117.096019</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>843.091335</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2154.566745</td>\n",
       "      <td>42700</td>\n",
       "      <td>...</td>\n",
       "      <td>3.512881</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.170960</td>\n",
       "      <td>0.702576</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.810304</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.234192</td>\n",
       "      <td>4.215457</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>135.135135</td>\n",
       "      <td>67.567568</td>\n",
       "      <td>67.567568</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>945.945946</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2027.027027</td>\n",
       "      <td>14800</td>\n",
       "      <td>...</td>\n",
       "      <td>2.702703</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.702703</td>\n",
       "      <td>1.351351</td>\n",
       "      <td>2.702703</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.378378</td>\n",
       "      <td>0.0</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    num_questions  num_periods  num_commas  num_exclam  num_quotes  num_colon  \\\n",
       "Id                                                                              \n",
       "1       56.818182    56.818182   28.409091         0.0    0.000000        0.0   \n",
       "2       90.634441    30.211480   60.422961         0.0   60.422961        0.0   \n",
       "3      175.438596    87.719298   87.719298         0.0    0.000000        0.0   \n",
       "5       70.257611    46.838407  117.096019         0.0    0.000000        0.0   \n",
       "7      135.135135    67.567568   67.567568         0.0    0.000000        0.0   \n",
       "\n",
       "      num_stops  num_semicolon    num_words  num_chars  ...      NOUN  NUM  \\\n",
       "Id                                                      ...                  \n",
       "1   1022.727273            0.0  2073.863636      35200  ...  3.693182  0.0   \n",
       "2   1087.613293            0.0  2024.169184      33100  ...  4.229607  0.0   \n",
       "3   1491.228070            0.0  2719.298246      11400  ...  4.385965  0.0   \n",
       "5    843.091335            0.0  2154.566745      42700  ...  3.512881  0.0   \n",
       "7    945.945946            0.0  2027.027027      14800  ...  2.702703  0.0   \n",
       "\n",
       "        PART      PRON     PROPN     PUNCT  SCONJ       SYM      VERB    X  \n",
       "Id                                                                          \n",
       "1   1.136364  1.420455  0.000000  1.988636    0.0  0.000000  5.113636  0.0  \n",
       "2   0.000000  1.510574  0.000000  2.416918    0.0  0.000000  2.719033  0.0  \n",
       "3   0.000000  4.385965  0.000000  3.508772    0.0  0.000000  7.894737  0.0  \n",
       "5   1.170960  0.702576  0.000000  2.810304    0.0  0.234192  4.215457  0.0  \n",
       "7   0.000000  2.702703  1.351351  2.702703    0.0  0.000000  3.378378  0.0  \n",
       "\n",
       "[5 rows x 30 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[feature_arr].head()*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_feature_vector_and_label(df, feature_names):\n",
    "    \"\"\"\n",
    "    Generate input and output vectors using the vectors feature and\n",
    "     the given feature names\n",
    "    :param df: input DataFrame\n",
    "    :param feature_names: names of feature columns (other than vectors)\n",
    "    :return: feature array and label array\n",
    "    \"\"\"\n",
    "    features = df[feature_names].astype(float)\n",
    "    labels = df[\"Score\"] > df[\"Score\"].median()\n",
    "    return features, labels\n",
    "\n",
    "X_train, y_train = get_feature_vector_and_label(train_df, feature_arr)\n",
    "X_test, y_test = get_feature_vector_and_label(test_df, feature_arr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False    3483\n",
       "True     2959\n",
       "Name: Score, dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1529, 30)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We train a model using `sklearn`, and measure its performance using the methods we've covered before."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "clf = RandomForestClassifier(n_estimators=100, class_weight='balanced', oob_score=True)\n",
    "clf.fit(X_train, y_train)\n",
    "\n",
    "y_predicted = clf.predict(X_test)\n",
    "y_predicted_proba = clf.predict_proba(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training accuracy = 0.580, precision = 0.553, recall = 0.440, f1 = 0.490\n"
     ]
    }
   ],
   "source": [
    "def get_metrics(y_test, y_predicted):  \n",
    "    # true positives / (true positives+false positives)\n",
    "    precision = precision_score(y_test, y_predicted, pos_label=True,\n",
    "                                    average='binary')             \n",
    "    # true positives / (true positives + false negatives)\n",
    "    recall = recall_score(y_test, y_predicted, pos_label=True,\n",
    "                              average='binary')\n",
    "    \n",
    "    # harmonic mean of precision and recall\n",
    "    f1 = f1_score(y_test, y_predicted, pos_label=True, average='binary')\n",
    "    \n",
    "    # true positives + true negatives/ total\n",
    "    accuracy = accuracy_score(y_test, y_predicted)\n",
    "    return accuracy, precision, recall, f1\n",
    "\n",
    "\n",
    "\n",
    "# Training accuracy\n",
    "# Thanks to https://datascience.stackexchange.com/questions/13151/randomforestclassifier-oob-scoring-method\n",
    "y_train_pred = np.argmax(clf.oob_decision_function_,axis=1)\n",
    "\n",
    "accuracy, precision, recall, f1 = get_metrics(y_train, y_train_pred)\n",
    "print(\"Training accuracy = %.3f, precision = %.3f, recall = %.3f, f1 = %.3f\" % (accuracy, precision, recall, f1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation accuracy = 0.576, precision = 0.587, recall = 0.452, f1 = 0.511\n"
     ]
    }
   ],
   "source": [
    "accuracy, precision, recall, f1 = get_metrics(y_test, y_predicted)\n",
    "print(\"Validation accuracy = %.3f, precision = %.3f, recall = %.3f, f1 = %.3f\" % (accuracy, precision, recall, f1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's save our new model and vectorizer to disk so we can use it later."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['../models/model_3.pkl']"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_path = Path(\"../models/model_3.pkl\")\n",
    "joblib.dump(clf, model_path) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Validating that features are useful\n",
    "\n",
    "Let's look at feature importances to validate that our new features are being used by the new model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_feature_names = np.array(feature_arr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Top 20 importances:\n",
      "\n",
      "num_chars: 0.053\n",
      "num_periods: 0.052\n",
      "num_questions: 0.051\n",
      "num_diff_words: 0.049\n",
      "ADJ: 0.048\n",
      "DET: 0.048\n",
      "ADV: 0.048\n",
      "VERB: 0.046\n",
      "NOUN: 0.045\n",
      "ADP: 0.045\n",
      "num_commas: 0.044\n",
      "PUNCT: 0.043\n",
      "num_stops: 0.043\n",
      "PRON: 0.043\n",
      "PART: 0.043\n",
      "polarity: 0.042\n",
      "num_words: 0.041\n",
      "avg_word_len: 0.041\n",
      "PROPN: 0.033\n",
      "NUM: 0.027\n",
      "\n",
      "Bottom 20 importances:\n",
      "\n",
      "num_commas: 0.044\n",
      "PUNCT: 0.043\n",
      "num_stops: 0.043\n",
      "PRON: 0.043\n",
      "PART: 0.043\n",
      "polarity: 0.042\n",
      "num_words: 0.041\n",
      "avg_word_len: 0.041\n",
      "PROPN: 0.033\n",
      "NUM: 0.027\n",
      "num_colon: 0.023\n",
      "num_quotes: 0.02\n",
      "AUX: 0.016\n",
      "SYM: 0.015\n",
      "INTJ: 0.015\n",
      "X: 0.011\n",
      "num_semicolon: 0.0077\n",
      "num_exclam: 0.0072\n",
      "CONJ: 0\n",
      "SCONJ: 0\n"
     ]
    }
   ],
   "source": [
    "k = 20\n",
    "print(\"Top %s importances:\\n\" % k)\n",
    "print('\\n'.join([\"%s: %.2g\" % (tup[0], tup[1]) for tup in get_feature_importance(clf, all_feature_names)[:k]]))\n",
    "\n",
    "print(\"\\nBottom %s importances:\\n\" % k)\n",
    "print('\\n'.join([\"%s: %.2g\" % (tup[0], tup[1]) for tup in get_feature_importance(clf, all_feature_names)[-k:]]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Comparing predictions to data\n",
    "\n",
    "This section uses the evaluation methods described in the Comparing Data To Predictions [notebook](https://github.com/hundredblocks/ml-powered-applications/blob/master/notebooks/comparing_data_to_predictions.ipynb) on this third model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "get_roc_plot(y_predicted_proba[:,1], y_test, figsize=(10,10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x648 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "get_confusion_matrix_plot(y_predicted, y_test, figsize=(9, 9))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No handles with labels found to put in legend.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\tBrier: 0.241\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x648 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "get_calibration_plot(y_predicted_proba[:,1], y_test, figsize=(9,9))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This third model only uses features that can be easily interpreted, and is better calibrated than previous models. This makes it a very good candidate for our application (feel free to look at `Comparing Models` for a more detailed comparison)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Running Inference\n",
    "\n",
    "Just like for our first two models, we define an inference function that takes in an arbitrary question and outputs an estimated probability of it receiving a high score according to our model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 1/1 [00:00<00:00, 87.70it/s]\n",
      "100%|██████████| 1/1 [00:00<00:00, 662.19it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.38 probability of the question receiving a high score according to our model\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "pos_prob = get_question_score_from_input(\"\"\"\n",
    "When quoting a person's informal speech, how much liberty do you have to make changes to what they say?\n",
    "\"\"\")\n",
    "\n",
    "print(\"%s probability of the question receiving a high score according to our model\" % (pos_prob))"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "ml_editor",
   "language": "python",
   "name": "ml_editor"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
